Evaluation of bacterial infections in organ transplantation

Hospital das Clinicas da Faculdade de Medicina da Universidade de Sao PauloHospital das Clinicas da Faculdade de Medicina da Universidade de Sao Paul

Evaluation of a Droplet Digital Polymerase Chain Reaction Format for DNA Copy Number Quantification

ABSTRACT: Droplet digital polymerase chain reaction (ddPCR) is a new technology that was recently commercialized to enable the precise quantification of target nucleic acids in a sample. ddPCR measures absolute quantities by counting nucleic acid molecules encapsulated in discrete, volumetrically defined, water-in-oil droplet partitions. This novel ddPCR format offers a simple workflow capable of generating highly stable partitioning of DNA molecules. In this study, we assessed key performance parameters of the ddPCR system. A linear ddPCR response to DNA concentration was obtained from 0.16 % through to 99.6 % saturation in a 20,000 droplet assay corresponding to more than 4 orders of magnitude of target DNA copy number per ddPCR. Analysis of simplex and duplex assays targeting two distinct loci in the Lambda DNA genome using the ddPCR platform agreed, within their expanded uncertainties, with values obtained using a lower density microfluidic chamber based digital PCR (cdPCR). A relative expanded uncertainty under 5 % was achieved for copy number concentration using ddPCR. This level of uncertainty is much lower than values typically observed for quantification of specific DNA target sequences using currently commercially available real-time and digital cdPCR technologies

Searching for molecular markers in head and neck squamous cell carcinomas (HNSCC) by statistical and bioinformatic analysis of larynx-derived SAGE libraries

Background: Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignancies in humans. The average 5-year survival rate is one of the lowest among aggressive cancers, showing no significant improvement in recent years. When detected early, HNSCC has a good prognosis, but most patients present metastatic disease at the time of diagnosis, which significantly reduces survival rate. Despite extensive research, no molecular markers are currently available for diagnostic or prognostic purposes. Methods: Aiming to identify differentially-expressed genes involved in laryngeal squamous cell carcinoma (LSCC) development and progression, we generated individual Serial Analysis of Gene Expression (SAGE) libraries from a metastatic and non-metastatic larynx carcinoma, as well as from a normal larynx mucosa sample. Approximately 54,000 unique tags were sequenced in three libraries. Results: Statistical data analysis identified a subset of 1,216 differentially expressed tags between tumor and normal libraries, and 894 differentially expressed tags between metastatic and non-metastatic carcinomas. Three genes displaying differential regulation, one down-regulated (KRT31) and two up-regulated (BST2, MFAP2), as well as one with a non-significant differential expression pattern (GNA15) in our SAGE data were selected for real-time polymerase chain reaction (PCR) in a set of HNSCC samples. Consistent with our statistical analysis, quantitative PCR confirmed the upregulation of BST2 and MFAP2 and the downregulation of KRT31 when samples of HNSCC were compared to tumor-free surgical margins. As expected, GNA15 presented a non-significant differential expression pattern when tumor samples were compared to normal tissues. Conclusion: To the best of our knowledge, this is the first study reporting SAGE data in head and neck squamous cell tumors. Statistical analysis was effective in identifying differentially expressed genes reportedly involved in cancer development. The differential expression of a subset of genes was confirmed in additional larynx carcinoma samples and in carcinomas from a distinct head and neck subsite. This result suggests the existence of potential common biomarkers for prognosis and targeted-therapy development in this heterogeneous type of tumor.Fundação de Amparo a Pesquisa do Estado de São Paulo/FAPESP [05/51467-0]; [04/12054-9]; [07/50894-7]Ludwig Institute for Cancer ResearchConselho Nacional de Pesquisas/CNPqCoordenacao de Aperfeicoamento do Pessoal do Ensino Superior/CAPE

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio